Change detection has been an important research topic in the field of pattern recognition, machine learning, data mining, and other related areas. Detection of changes in a video stream can be used to identify abnormal events or suspicious activities. Changes in a large amount of data in a continuous data stream can be used for fault detection or prediction.
In one application of change detection, an anomaly in data readings of a machine is used to alarm the user before a serious problem develops. That application is known as the task of machine condition monitoring. Machine condition monitoring presents the challenge of detecting change amid multiple sensors by quickly analyzing large amounts of real-time data. One difficulty is that different sensors may or may not be related to each other. That may necessitate the selection or grouping of relevant sensors while monitoring certain faults. In practice, the determination of dependencies between sensors is not always obvious.
One-class support vector machines (SVMs) have been used in this application. SVMs are a set of related supervised learning methods used for classification and regression. Input data is viewed as two sets of vectors in an n-dimensional space. The SVM constructs a separating hyperplane in that space, which maximizes the margin between the two data sets. SVM does not take into account significant temporal information during the learning process, limiting its ability to detect certain anomalies that are common in practice. Further, correlation analysis is often needed to consider the relationship of plural sensors. The procedure is calculation-intensive and therefore not ideal for real-time monitoring of data-intensive streams.
There is therefore presently a need for a method and system for efficient real-time monitoring of data streams from plural interrelated machine sensors with the ability to detect changes or anomalies. To the inventors' knowledge, no such techniques are currently available.